1. Technical Field
This invention concerns an apparatus and method for representative sampling, thermal conditioning, and analysis of laboratory quantities of non-homogeneous petroleum hydrocarbon samples such as crude oil and heavy oils.
2. Background Information
Physical and chemical properties of hydrocarbon streams in petroleum refining and petrochemical processes can be measured by a variety of technologies and methods. Such measurements represent information that can be used to by engineers to maintain throughput near the design limits of an operating unit, achieve property target values, maximize yields for a given feed, and minimize energy costs. However, attainment of process control and optimization objectives may be constrained when process samples are captured manually and submitted for laboratory analysis: results correspond to the point in time when the sample was captured; some properties or component concentrations may change before laboratory analysis if special care is not exercised during sampling or transport; and the frequency of manual sampling and analysis may be insufficient to detect important process changes. This fact has motivated the repackaging and automation of many laboratory methods to permit their implementation for online measurement. Most common are univariate analyzers in which the sensor element responds directly to the property or component of interest; water and sulfur content, viscosity, and density, are examples. With such sensors, a mathematical formula as simple as a linear equation calculates the property value of interest from the sensor's associated response, which, mathematically speaking, is a discrete function e.g. voltage, frequency, absorbance, conductivity, or simply intensity. Calibration is generally simple and can be performed easily online.
Though discrete analyzers are relatively inexpensive, convenient to install, and simple to operate, the very selectivity that makes them useful also tends to limit the extent to which they can enable rigorous optimization of processes that process and transform complex chemical mixtures. The issue is one of information content, or more precisely, the degrees of freedom in the mixture. Thus, hydrocarbon streams such as motor fuels, the components blended to produce them, and crude oil that is refined to produce the blending components are themselves complex mixtures that do not lend to full characterization with simple, discrete analyzers. Gasoline, for example, may contain hundreds of components while the number of components in crude oil typically exceeds 10,000. Accordingly, refinery process optimization may depend on measuring a property that itself derives from multiple parameters or components. A common example of this is the octane value of gasoline. Though traditionally measured with a knock engine as a simple, discrete property, octane is a function of the interplay between many variables including the proportions of aromatic, olefinic, paraffinic and isoparaffin compounds and molecular weight distribution. Another type of multivariate property is one that is not a single value, but an ensemble of related values. For example, the performance of gasoline and diesel as motor fuels depends on the temperatures at which certain percentages of the components distill from the mixture, hence the importance of the so-called T10, T50, and T90 values, i.e. the temperatures at which 10%, 50%, and 90% of the mixture distills under defined experimental conditions.
Given the central importance of distillation properties in regard to petroleum products, it follows that this is also the case for materials from which they are produced. Indeed, the true boiling point (TBP) distillation curve for crude oil feed affects the economics of the oil refining process through its direct impact on total throughput, the mix of products that can be produced, and energy consumed by the process. Sometimes referred to as the distillation curve or profile, each particular type of crude oil has an associated TBP curve, which is a plot relating distillation temperature and the percentage of material in the crude oil that has been distilled. A representative distillation curve for a hydrocarbon mixture is shown in FIG. 1.
Unfortunately, conventional methods for generating TBP distillation curves from crude oil samples involve the actual distillation of the sample in the laboratory using carefully calibrated apparatus that lends to a very limited level of automation and generally must be carried out by skilled technicians. Requiring 3-5 liters of sample and up to several days' time, this approach does not support rapid decision-making required when receiving crude oil from a ship or pipeline, or when feeding crude oil to the crude distillation unit (CDU) in the refinery, the first of many steps in the oil refining process. Refiners therefore have made attempts to ply analytical technologies like near infrared (NIR) spectroscopy in the hope of realizing benefits similar to those afforded by its application for multivariate analysis of hydrocarbon streams such as in gasoline and diesel, which are homogeneous, clear, and chemically simpler than crude oil.
NIR spectrometers belong to a class of advanced analyzers whose base response can be described in mathematical parlance as being a continuous function, a vector, an array, or a matrix. Rather than being one or several independent data points, the output of such analyzers comprises dozens or hundreds of points that define a continuous, complex function whose features are determined by the physical characteristics and chemical composition of the sample. Other examples of advanced analyzer technologies include NMR, FTIR, and Raman spectrometers. In some cases chromatographic data and outputs obtained through sample stimulation by microwave or ultrasonic signals also may have the form of a multivariable response matrix. The terms applied to such outputs describe the technology of origin, e.g. spectrometers and chromatographs yield spectra and chromatograms, respectively, which are x-y plots relating dependent responses measured across a continuum of independent values such as time, frequency, or wavelength. The general term response matrix will be used hereinafter when referring to outputs obtained from advanced analyzers generally, while spectrum may be used alternatively for those obtained from a spectrometer.
Generally, the components or properties of complex hydrocarbon mixtures being analyzed do not express themselves explicitly in sample spectra. For example, in the application of NIR spectroscopy to measure motor fuel properties, no single feature accounts exclusively for octane value, cetane index, or the temperatures for distillation yields mentioned previously. Rather, such properties are determined by the relative quantities of hundreds or thousands of compounds in the mixture, which express themselves across a relatively broad spectral range. Accordingly, multivariable property models must be developed which yield the property value of interest when applied to the entire response matrix of an unknown sample or to significant portions thereof. The terms property and component may be used interchangeably or in combination hereinafter to denote any of the following: the characteristics of the aggregate mixture, e.g. its density, response matrix, or spectrum; subsets of compounds in a mixture which share common physical characteristics, e.g. boiling point range; compounds sharing chemical functionality, e.g. paraffins, naphthenes, aromatics, and asphaltenes; and individual compounds, e.g. toluene and hexadecane. Generally, a component is understood to be an isolable compound or group of compounds that share common chemical functionality or physical properties; whereas a property is understood to be a physical attribute of either the hydrocarbon mixture as a whole or of a component.
While these advanced analyzers/spectrometers have been used with broad success to measure properties of relatively light process streams that also are substantially homogeneous, this is not the case for heavy hydrocarbon streams such as crude and heavy oils that are similarly dark and inhomogeneous. Raman, for example, has been recognized as being incompatible with these oils due to the phenomenon of autoabsorption by black asphaltene (or carbonaceous) particles of Raman-effect photons. These same particles adversely impact NIR spectroscopy.
A need therefore exists, for an improved system and method for analyzing the content of non-homogeneous petroleum hydrocarbon samples such as crude oil and heavy oils.